1. Field of the Invention
The present invention relates to a method and apparatus for quantifying the response thresholds of biological and nonbiological systems to stimuli. In particular, the present invention relates to the determination of response thresholds using Recurrence Quantitation Analysis and other mathematical techniques, for perceivable and nonperceivable stimuli (light, sound, temperature, pressure, aroma, chemical, biochemical, learning tasks, electric and magnetic fields, and so forth).
2. Discussion of Background
Biological systemsxe2x80x94ranging from cells in culture media to laboratory animals to human beingsxe2x80x94exhibit dynamically complex responses to stimuli. The classic approach to studying stimulus-response relationships has been to present the stimulus in a simplified experimental setting, with as few variables as possible, and record the subject""s response. Various physiological parameters were also measured in order to provide an objective measure of the subject""s response, including skin resistance and potential, blood pressure, respiration rate, electrocardiogram (ECG or EKG), electroencephalogram (EEG), electromyogram (EMG), etc. This approach was historically useful in determining thresholds for readily-perceivable stimuli such as light, sound, and pressure, as well as other stimuli that were specifically identifiable and recognizable as stimuli. However, the classic methodology fails when the stimulus is one that the subject does not perceive directly, or which does not lend itself to association with straightforward measurements of physiological parameters.
Recent developments in the study of physiological systems (including humans) draw on chaos theory and informational theory, suggesting that these are complex systems that exhibit equally complex dynamic, state-dependent behaviors. Unlike classic physiology, which implicitly assumed that physiological systems are both linear and homeostatic, the new paradigm acknowledges that the experimenter""s underlying assumptions can affect both the design and the outcome of any experimental study. (For purposes of this specification, the term xe2x80x9chomeostaticxe2x80x9d refers to the tendency of a biological system to maintain internal constancy regardless of its surrounding environment. xe2x80x9cNonhomeostaticxe2x80x9d is the reverse of homeostatic.) This approach has led to the development of new mathematical tools for the study of complex, nonlinear behaviors in biological systems.
Many different types of stimuli entail physiological changes that are straightforward to measure and analyze. For example, thresholds of perception for light, sound, and pressure are measured by standard, well-established techniques. Similarly, the effects of medications on blood pressure, blood chemistry, heart rate, etc. can be measured with readily-available instrumentation, and can be analyzed using familiar statistical techniques. The difficulty arises in measuring the effects of stimuli that elicit nonlinear, nonhomeostatic responses. Many environmental parameters are believed to elicit such nonlinear responses, including but not necessarily limited to electric and magnetic fields, light, and sound.
Various approaches to the study of stimulus effects have been developed. By way of example, Metz, et al. (U.S. Pat. No. 5,051,209) measures the effects of external stimuli on the brain using positron emission topography (PET). Stimuli include psychoactive compounds, drugs, and sensory-perceivable environmental factors (temperature, noise, vibration, light). The method includes the following steps: while controlling behavioral influences on the subject""s brain via a xe2x80x9cbehavioral clamp,xe2x80x9d (1) measuring cerebral metabolism with a positron emission tomography (PET) scan in the absence of external treatment; (2) administering an external treatment to the subject, (3) measuring cerebral metabolism after the treatment, and (4) determining any differences between the cerebral metabolism in steps (1) and (3). The PET image sets are standardized so the data from different subjects, and from the same subject at different times, can be compared to determine which (if any) brain areas show differences between the treatment conditions.
Electroencephalographic (EEG) measurements are frequently used to study responses to various stimuli. By way of example, Levin (U.S. Pat. No. 5,860,936) provides a differential geometric method and an apparatus for measuring an observer""s perception of dynamically evolving visual and auditory stimuli. The apparatus includes a stimulus output device for presenting stimuli to the observer, and a stimulus manipulation device that permits the observer to modify the presented stimuli by selecting related stimuli from a database. The method is based on the concepts of xe2x80x9creference stimulixe2x80x9d and xe2x80x9creference transformationsxe2x80x9d: an evolving stimulus is described in terms of an initial reference state and of reference transformations (for example, movement in a certain distance for a certain direction, change in color, etc. The observer is asked to identify which small transformation of a first stimulus is perceived to be equivalent to a small transformation of a second stimulus. The observer""s perceptions of a number of such transformations are used to calculate an affine connection on the stimulus manifold, which encodes how that particular observer perceives evolving stimuli as sequences of reference transformations. Differences between observers characterize differences between their individual perceptions, and can be used to xe2x80x9ctranslatexe2x80x9d sets of stimuli between observers).
Maynard (U.S. Pat. No. 5,816,247) uses neural networks to analyze EEG data with an apparatus that includes circuitry for receiving, amplifying and processing EEG signals, a computer for statistical processing of the signals, a neural network with a training mode and a classifying mode, a display for displaying the output classifying values in n-dimensional form, and a memory for storing the data. The method includes the following steps: (1) obtaining sets of training data from a plurality of patients; (2) determining categories for classifying the sets; (3) defining an n-dimensional space in which the determined categories are represented as distinct coordinates; (4) training a neural network with n outputs using the sets as input data and the defined categories as target outputs; and (5) applying a set of EEG data from a patient to the neural network to derive a position in the n-space for the purpose of assessing a patient category.
Nadel (U.S. Pat. No. 5,788,648) shows an EEG apparatus for exploring a subject""s response to quantifiable external stimuli (oral, visual, tactile, acoustic, and/or olfactory, and combinations thereof). The apparatus includes sensing apparatus for sensing brainwave signals, stimulating apparatus for generating the stimuli, and processing apparatus for receiving and processing the signals to compute a correlation quotient of the signals and the stimuli. Data analysis is based on Fourier transforms.
Urbach, et al. (U.S. Pat. No. 5,282,475) disclose an apparatus and method for objective determination of auditory thresholds for intelligible speech. The apparatus includes a microprocessor, a digital-to-analog converter and a signal amplifier for presenting a randomly-selected speech stimulus to a human subject, a multichannel EEG amplifier for monitoring the physiological response of the subject, and a CRT display. The stimulus is presented to the subject through earphones while monitoring the EEG, and recorded data are corrected for eye movement artifacts.
Rosenfeld (U.S. Pat. No. 5,137,027) provides a method for using event-related potentials (P300 brain wave amplitude and/or latency) to evaluate whether or not a subject has previously performed a given act. Subjects are exposed to two successive groups of perceivable stimuli, each group designed according to a specified protocol. The following parameters are computed and compared for the two stimulus groups: (1) the prestimulus EEG potential for a first measured time interval (50-500 msec); and (2) the maximum voltage amplitude of the P3 wave produced over a second time interval (also 50-500 msec), where the P3 wave is measured within 300-1200 msec from the start of exposure to the stimulus.
Prichep (U.S. Pat. No. 5,083,571) discloses a system for using discriminant analysis of EEG data for diagnostic purposes, specifically, to automatically evaluate the probability that a patient belongs to a specified diagnostic category (or a subtype within a category). The EEG data is processed via standard techniques to obtain factor Z scores which are used to distinguish among different clinical conditions via discriminant analysis.
John applies factor analysis and Z-transformations to EEG data. U.S. Pat. No. 4,913,160 deals with the measurement of averaged evoked responses (AERs) while presenting a programmed sequence of stimuli to a subject. The AERs are analyzed using factor analysis, where each waveshape is digitized, decomposed by factor analysis, Z-transformed and compared to results from a normal population. Results are displayed as color-coded topographic maps of the head showing the locations of the electrodes (different colors represent the degree of abnormality reflected by the Z-scores). U.S. Pat. No. 4,201,224 describes a method and system for the quantitative description of human abnormal brain states. EEG data is recorded while a multimodal stimulator provides signals aimed at different sensory systems (clicks, flashes, etc.). The patient""s data are processed and compared with previously stored normative data that describe brain states in patients without head trauma. Data processing includes a set of Z-transformations that define a brain state vector: the length, direction a n change of this vector over time provide an evaluation of the anatomical location of any brain damage, the severity of functional impairment, and the rate of improvement or deterioration of the patient""s state.
Gevins, et al. (U.S. Pat. No. 4,736,751) provide a method and system for statistical analysis of brain wave activity (evoked potentials) using a fairly large number of scalp electrodes. The locations of the electrodes are digitally recorded and stored in computer memory, then the subject is exposed to a set of stimuli while recording brain waves, magnetoencephalographic waves, and eye movement data. These data are analyzed to determine the brain sites which give rise to the activity; data can be displayed on a three-dimensional model. The signal-to-noise ratio is improved by eliminating data with excessive noise contamination, using bandpass filters, and spatial deconvolution analysis to reduce the distortion of brain waves due to transmission through the cerebrospinal fluid, skull, and scalp.
Shevrin, et al. (U.S. Pat. No. 4,699,153) use a form of nonperceived stimulus (subliminal images) to assess verbal psychobiological correlates with EEG data. The stimulus consists of a plurality of words which are selected to be in four categories (pleasant words, unpleasant words, words related to a diagnosed conscious pathological condition, and words related to a diagnosed unconscious pathological condition). The words are presented by tachistoscope in both subliminal and supraliminal modes of operation (1 msec and 30 msec duration, respectively). Evoked responses are analyzed using a technique based on information signal theory).
Bergelson, et al. (U.S. Pat. No. 4,493,327) disclose a method and system for automatic evoked potential detection. The method consists of deriving sets of averaged brain waveforms in the presence and absence of a stimulus, computing (via Fast Fourier Transform or FFT) a test measure that is related to the averaged spectral characteristics and variance of each set, and evaluating the test measures to ascertain the likelihood that the:patient exhibited a significant evoked potential in response to the stimulus.
Sato, et al. (U.S. Pat. No. 4,214,591) provide a brain wave analyzing system and method for automatically identifying a spectrum pattern obtained by autoregressive model analysis. The method relies on the fact that brain waves include a part that is connected with some past activities of the brain and a part that is not. A series of standard brain waves are measured, digitized, and processed to obtain the mean and variance vectors of the autoregressive coefficient vectors of the digital data. The brain waves to be examined are processed in the same fashion. The distances between the vectors of the experimental and standard brain waves are then compared.
Duffy (U.S. Pat. No. Re. 34,015) generates topographic displays derived from EEG data (evoked potentials resulting from pseudorandom stimuli) for the purpose of diagnosing brain disorders. The EEG data is digitized and transformed to spectral data via FFT. Data reflecting movement artifacts and high frequency noise are eliminated, and the reduced data is topographically displayed. The data can be analyzed using significance probability mapping to identify significant brain activity features related to various neurophysiological conditions, grid sector analysis to produce numerical measures of the degree of global or focal deviations from normal, and coefficient of variation analysis to find head regions where there are wide variations in brain activity.
Rickards (U.S. Pat. No. 4,462,411) measures auditory responses with an evoked response audiometer that supplies a signal modulated by a continuous frequency waveform in order to evoke phase-locked steady state potentials in the subject. The subject""s EEG is recorded, analyzed using Fourier transforms, and the relevant spectral components are used to measure the degree of hearing loss.
Thornton, et al. (U.S. Pat. No. 4,275,744) disclose an auditory response detection method and apparatus that uses tone burst signals. Their system is based on the observation that observations of the EEG signal made at selected intervals following an auditory stimulus can provide a statistical estimate of the likelihood of a response being actually present in an individual subject. The EEG data is monitored, filtered to remove frequencies above and below the range of interest, and sampled at predetermined times after the auditory signal has been applied to the subject. The apparatus provides an output signal when the polarity of the sample signal matches the expected polarity at the predetermined times.
Fisher, et al. (U.S. Pat. No. 5,673,703) describe an apparatus and method for automated testing of vibrotactile responses. The apparatus includes a computer-controlled stimulation probe that applies a steady, reproducible stimulus; the computer also records the subject""s response.
Tuckett, et al. (U.S. Pat. No. 5,381,805) disclose a computerized apparatus for testing cutaneous responses to heat, pricking, indentation, vibration, and two-point discrimination. A programmable computer controls the operation and the apparatus and records the patient""s responses.
Young, et al. (U.S. Pat. No. 5,143,081) apply a train of paired stimuli to a biological system, and measure and analyze the response. The first of each pair of stimuli (the conditioning stimulus) is applied at randomly varying intensities and at a constant frequency; the second of each pair (the test stimulus) is applied at a constant intensity and a randomly varying frequency. Measurements can be made on the neural, cardiovascular, skeletal muscle, visual, auditory, secretory, renal, hepatic, gastrointestinal, and genito-urinary systems.
Strian, et al. (U.S. Pat. No. 4,763,666) provide a method and apparatus for determining the thermal sensitivity or pain threshold. Their method consists of establishing upper and lower temperature limits for perceived pain, then applying random-temperature thermal stimuli to the skin to verify the reported pain thresholds.
Additional techniques such as recurrence quantitation analysis and principal components analysis have been developed to aid in the detection of signals in data sequences, especially nonlinear or chaotic signals that are not amenable to traditional linear analysis. These techniques are described in the following publications, which are incorporated herein by reference: xe2x80x9cRecurrence quantification analysis and principal components in the detection of short complex signals,xe2x80x9d J. P. Zbilut, A. Giuliani and C. L. Webber, Jr., Physics Letters A, Vol. 237 (1998), pp. 131-135; xe2x80x9cDynamical assessment of physiological systems and states using recurrence plot strategies,xe2x80x9d C. L. Webber, Jr. and J. P. Zbilut, J. Applied Physiology, Vol. 76 (1994), pp. 965-973. These techniques permit identification and analysis of time correlations (xe2x80x9crecurrencesxe2x80x9d) in physiological data that are not apparent in the time series measurements.
Despite the many different techniques available to researchers, there remains a need for a reliable methodology for determining the thresholds of physiological stimuli, whether or not the stimuli are perceived by the subject.
According to its major aspects and broadly stated, the present invention includes a method and apparatus for evaluating the response of a subject (or other biological or nonbiological system) to an external stimulus (optical, thermal, tactile, auditory, taste, electrical, magnetic, pharmacological, hormonal, chemical, biochemical, etc.) or to a stimulus that is generated internally (local changes in oxygen concentration, transformation; of cells from normal to malignant, localized tissue injury, localized inflammation, etc.). The subject is exposed to the stimulus while at least one electrophysiological signal (EEG, EKG, EMG, electrical potential, blood oxygen saturation, etc.) is recorded. The recorded data is digitized, stored, and analyzed to ascertain the properties of the subject""s response to the stimulus. The significance of the results can be analyzed using parametric or nonparametric statistical procedures. The method is independent of well-known constraints such as the size of the data set, underlying assumptions regarding the statistical distribution of the data, and assumptions about the nature of the data (homeostatic vs. nonhomeostatic, linear vs. nonlinear, etc.). It permits reliable analysis of a subject""s response regardless of whether or not the subject is aware of the presence (or absence) of the stimulus being evaluated, and regardless of whether the stimulus is external or internal. Furthermore, the invention permits detection of structure in signals whether or not the signals were elicited by periodic or nonperiodic stimuli.
The apparatus (to be described in detail below) may include equipment for generating stimuli, recording output signals from the subject, and recording, processing, and analyzing the output signals. Unwanted portions of the measured output signals (for example, the relatively high-amplitude signals associated with the subject""s EMG and respiration that appear during measurements of the subject""s EEG) are removed by a numerical filter or other suitable filter. The apparatus also includes a phase-space filter that removes non-useful portions of the output signals in phase space, an analog-to-digital (AID) converter, and a programmable computer for carrying out recurrence procedures that further facilitate analysis of the data.
The invention can be used with either controlled or uncontrolled stimuli (also termed xe2x80x9cinputsxe2x80x9d). Importantly, it permits reliably distinguishing the subject""s response in the form of an output signal of interest from other, generally stronger, signals that may obscure it. For example, the voltage from a surface electrode placed on a selected location on a subject""s skin can be measured over a period of time. When these measurements are analyzed, the voltage shows a trend (either up or down) that is related to electrode polarization effects. In addition, there is an approximately periodic signal that corresponds to the subject""s heart beat (the EKG or ECG), signals that correspond to the subject""s brain electrical activity (EEG), rhythmic signals that correspond to the subject""s breathing, and episodic signals that can be traced to contractions of the subject""s muscles during movement (EMG). Selected signals of this nature can be optimized depending on the placement of the electrode, the choice of frequency measured, and other well-known factors. In contrast, the invention. permits isolation of much smaller (usually by several orders of magnitude) electrical signals that represent the subject""s response to a stimulus of interest.
An important feature of the present invention is the use of Recurrence Quantification Analysis (xe2x80x9cRQAxe2x80x9d) or other suitable techniques to evaluate the output signals. Such techniques can be used to quantify and analyze nonhomeostatic responses to a variety of external and internal stimuli, including but not limited to light, sound, pressure, aroma, taste, electric and magnetic fields, chemical, biochemical, and hormonal stimuli, and internal changes on the cellular level. RQA, for example, involves digitizing the recorded data to obtain a scalar time series, which is then embedded in a multidimensional state space using a time delay to obtain a diagram that represents the evolution of the state of the system over time. This technique is particularly suitable for the study of systems that are characterized by nonhomeostatic transients and state changes.
Another important feature of the present invention is the use of numerical and phase-space filters to remove non-useful portions of the output signals. A numerical filter removes selected amplitudes, frequencies, or other components of the output signal; a phase-space filter removes selected points in phase space. Application of these filters prepares the data for recurrence analysis and increases the overall sensitivity of the analysis.
Another important feature of the invention is the apparatus used to implement the method. The apparatus may include equipment for generating a stimulus (xe2x80x9ccontrolled inputxe2x80x9d), measuring output signals from the subject, and storing and analyzing the resulting data. Depending on the nature of the system being examined, analyses may include parametric or nonparametric statistical analyses.
Another feature of the present invention is its applicability to any system that generates an output signal, either by virtue of a controlled or an uncontrolled input. In the latter case, it is necessary only that the output signal be generated by a subsystem of the system being examined. Thus, other parts of the same system can be analyzed for an output signal (or signals) that can serve as a control for the output signal from the subsystem of interest.
Still another feature of the present invention is its versatility. The invention can be used with substantially any types of stimulus and response, including but not necessarily limited to light, sound, temperature, pressure, aroma, taste, electric and magnetic fields, chemical and biochemical stimuli, hormonal stimuli, etc.
Yet another feature of the present invention is the ability to distinguish between the effects of the stimulus of interest and conditions (or other stimuli) that may be present. For example, the effects of subject fatigue and changes in ambient environmental conditions can be distinguished from the effects of the stimulus of interest.
Another feature of the present invention is the ability to detect and quantify output signals even when those signals form only a minuscule part of the measured signal, and even when the input is subliminal.